Instructions to use glopez/cifar-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use glopez/cifar-10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="glopez/cifar-10") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("glopez/cifar-10") model = AutoModelForImageClassification.from_pretrained("glopez/cifar-10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from glopez/cifar-10: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/glopez/cifar-10/resolve/main/training_args.bin
- Command line
-
hf download hf://glopez/cifar-10/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/glopez/cifar-10/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- 214fadefd5af082bcd37e72a242ed284e673d0da722b6f0a2a9f9f8df5ca0f58
- Size of remote file:
- 3.38 kB
- SHA256:
- 85de8c2443edd63bab0d98c36637cbba93f17087b2ab26eec8dbc45cf21313eb
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